Apparatus and methods of material identification are known that employ embedded algorithms for identifying unknown pure materials or mixtures (such pure materials or mixtures also referred to herein as “pure materials/mixtures”) based on the similarity between the spectra of such unknown pure materials/mixtures and the spectra of various known pure materials/mixtures stored in a database. For example, such apparatus and methods can be used to identify unknown pure materials/mixtures in diverse situations involving train derailments, overturned vehicles on roads or highways, industrial and chemical leaks or explosions, the illegal drug trade, etc. Further, such apparatus can include Raman spectrometers, infrared or near infrared spectrometers, fluorescence spectrometers, mass spectrometers, nuclear magnetic resonance (NMR) spectrometers, etc., which can employ algorithms configured to implement spectral database searching based on traditional cross correlation techniques, Euclidean techniques, derivative techniques, etc.
For example, conventional apparatus and methods of material identification that employ algorithms based on cross correlation techniques typically cross correlate the spectrum of an unknown pure material or mixture (such a pure material or mixture also referred to herein as a “pure material/mixture”) with the spectral data set for each known pure material/mixture stored in a database. Because the cross correlation of spectra having varying spectral intensity ranges often leads to erroneous results, such conventional apparatus and methods typically operate on spectral intensity values that have been normalized between 0 and 1. Such conventional apparatus and methods can then identify the closest match to the unknown pure material/mixture based on the spectral data set that provides the highest positive correlation coefficient (also referred to herein as the “hit quality index”).
Further, conventional apparatus and methods of material identification that employ algorithms based on Euclidean techniques typically subtract the spectral intensity value at each wavelength of the spectrum of an unknown pure material/mixture point-by-point from the spectral intensity value at each wavelength of the spectra of known pure materials/mixtures stored in a database. Such conventional apparatus and methods can sum the subtracted spectral intensity values to generate a hit quality index for each known pure material/mixture in the database. The conventional apparatus and methods can then identify the closest match to the unknown pure material/mixture based on the spectral data set that provides the lowest overall subtracted-sum value, which corresponds to the highest hit quality index.
Moreover, conventional apparatus and methods of material identification that employ algorithms based on derivative techniques typically subtract the derivative of the spectrum of an unknown pure material/mixture point-by-point from the derivative of the spectrum of each known pure material/mixture stored in a database. For example, such a derivative can be expressed as the difference between the spectral intensity value at a given wavelength “w” and the spectral intensity value at the wavelength “w−1”. Such conventional apparatus and methods can sum the subtracted derivative values to generate a hit quality index for each known pure material/mixture in the database. The conventional apparatus and methods can then identify the closest match to the unknown pure material/mixture based on the spectral data set that provides the lowest overall derivative subtracted-sum value, which corresponds to the highest hit quality index. It is noted that such derivative techniques can be used in combination with cross correlation techniques. Moreover, like the conventional apparatus and methods that employ algorithms based on the cross correlation technique, the conventional apparatus and methods that employ algorithms based on the Euclidean and derivative techniques typically operate on spectral intensity values that have been normalized between 0 and 1.
However, conventional apparatus and methods of material identification that employ algorithms based on traditional techniques, such as the cross correlation, Euclidean, and derivative techniques, can have drawbacks. For example, the spectrum of an unknown pure material/mixture can sometimes have a sloping baseline due to, e.g., problems with filtering, natural fluorescence in the unknown pure material/mixture, a trace contaminant that creates fluorescence in the unknown pure material/mixture, ambient light, laser bleed through, etc. Such a sloping baseline in the spectrum of an unknown pure material/mixture can, in turn, cause varying degrees of mathematical error and uncertainty in the results generated using the cross correlation, Euclidean, and derivative techniques discussed above.
Further, the spectrum of an unknown pure material/mixture can sometimes have poor signal-to-noise characteristics, causing spectral peaks to be barely recognizable amid the noise across the spectrum. Such poor signal-to-noise characteristics of the spectrum of an unknown pure material/mixture can also cause varying degrees of mathematical error and uncertainty in the results generated using the cross correlation, Euclidean, and derivative techniques discussed above, and can be effectively amplified by those techniques that involve a difference calculation.
In addition, to achieve increased success in material identification, it is typically necessary for conventional apparatus and methods to determine the similarity between the spectrum of an unknown pure material/mixture and the spectra of a multitude of known pure materials/mixtures (e.g., up to 100,000 or more) stored in a database. Because the spectrum for each unknown and known pure material/mixture can have up to 2000 or more spectral intensity values, the memory requirements for storing the spectra of the unknown and known pure materials/mixtures can be high. Likewise, the processing time required to execute algorithms using such large spectral data sets based on the traditional cross correlation, Euclidean, and derivative techniques can be high, making such algorithms impractical for use in portable (e.g., hand-held) spectroscopic instruments.